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Optimized Resource Allocation in IoT Using Fuzzy Logic and bio-inspired algorithms

WIRELESS PERSONAL COMMUNICATIONS 2023年第2期131卷 1393-1413页

作者： Sharma, Deepak Kumar Mishra, Jahanavi Singh, Aeshit Govil, Raghav Singh, Krishna Kant Singh, Akansha Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India Netaji Subhas Univ Technol Dept Informat Technol New Delhi India Amity Univ Uttar Pradesh Dept CSE ASET Noida India Bennett Univ Sch CSET Greater Noida India

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms-grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

关键词： Load balancing IoT Cloud computing Optimization bio-inspired algorithms Firefly algorithm GWO Fuzzy logic

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The adoption of socio- and bio-inspired algorithms for trust models in wireless sensor networks: A survey

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2018年第7期31卷 e3444-e3444页

作者： Nunoo-Mensah, Henry Boateng, Kwame Osei Gadze, James Dzisi Kwame Nkrumah Univ Sci & Technol Dept Comp Engn Kumasi Ghana Kwame Nkrumah Univ Sci & Technol Dept Elect & Elect Engn Kumasi Ghana

The security of a wireless sensor network is greatly increased with increasing levels of trustworthiness of nodes on the network. There are a number of trust models proposed that use various trust evaluation methods. The paper categorises trust evaluation methods for wireless sensor networks into socio-inspired, bio-inspired, and analytical methods. biologically inspired methods and socio constructs are rarely used in the design of trust schemes as opposed to analytical methods (probability, agent based, fuzzy, etc). The paper discusses existing bio- and socio-inspired-based trust schemes implemented for wireless sensor networks, viz quality-based distance vector protocol, enhanced bio-inspired trust and reputation model for wireless sensor network, bio-inspired trust routing protocol, machine learning-based bio-inspired trust and reputation model, socio-psychological trust and reputation model, and finally reputation framework for sensor network. The contributions, trust evaluation methods and limitations of recent trust models (i.e. analytical based, biological and socio-inspired models) are discussed in this paper. The pros and cons of analytical, socio, and biologically inspired algorithms for computing are outlined to easily ascertain the relevance of adopting either biological or socio-inspired approaches for solving trust-related problems in wireless sensor networks.

关键词： bio-inspired algorithms socio-inspired algorithms trust models wireless sensor network

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Solving ring loading problems using bio-inspired algorithms

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2011年第2期34卷 668-685页

作者： Bernardino, Anabela Moreira Bernardino, Eugenia Moreira Manuel Sanchez-Perez, Juan Antonio Gomez-Pulido, Juan Angel Vega-Rodriguez, Miguel Polytech Inst Leiria Sch Technol & Management Dept Comp Sci Res Ctr Informat & Commun P-2400 Leiria Portugal Univ Extremadura Polytech Sch Dept Technol Comp & Commun Caceres 10071 Spain

In the last years, several combinatorial optimisation problems have arisen in the communication networks field. In many cases, to solve these problems it is necessary the use of emergent optimisation algorithms. The Weighted Ring Loading Problem (WRLP) is an important optimisation problem in the communication optical network field. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. An optimal load balancing is very important, as it increases the system's capacity and improves the overall ring performance. The WRLP consists on the design, in a communication network of a transmission route (direct path) for each request, such that high load on the arcs/edges is avoided, where an arc is an edge endowed with a direction. In this paper we study this problem in two different ring types: Synchronous Optical NETworking (SONET) rings and Resilient Packet Ring (RPR). In RPR the purpose is to minimise the maximum load on the ring Arcs (WRALP). In SONET rings the purpose is to minimise the maximum load on the ring Edges (WRELP). The load of an arc is defined as the total weight of those requests that are routed through the arc in its direction and the load of an edge is the total weight of the routes traversing the edge in either direction. In this paper we study both problems without demand splitting and we propose three bio-inspired algorithms: Genetic Algorithm with multiple operators, Hybrid Differential Evolution with a multiple strategy and Hybrid Discrete Particle Swarm Optimisation. We also perform comparisons with other algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Communication networks bio-inspired algorithms Optimisation algorithms Swarm Intelligence Weighted Ring Arc-Loading Problem Weighted Ring Edge-Loading Problem

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Analysis of energy harvesting in SWIPT using bio-inspired algorithms

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INTERNATIONAL JOURNAL OF ELECTRONICS 2023年第2期110卷 291-311页

作者： Nair, Ajin R. Kirthiga, S. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Coimbatore Tamil Nadu India

In this paper, we consider a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system, which is having I and Q (in-phase and quadrature) imbalance hardware impairment. The prime objective of this paper is to maximize the harvested energy for a SWIPT PS system, satisfying a minimum signal to noise ratio (SNR) requirement for information signal processing in the presence of IQ imbalance hardware impairment. The paper uses four types of bio-inspired algorithms like Particle Swarm Optimisation, Artificial Bee Colony, Firefly Algorithm and Invasive Weed Optimisation to attain the maximum harvested energy considering parameters of PS ratio, amplitude and phase imbalances in a Rayleigh fading environment. The simulation results show that under ideal power conversion efficiency and minimum hardware impairments, a maximum SNR of 16.7 dB and harvested energy of 11 dB is achieved for a transmit power of 20 dB in SWIPT systems for IWO bio-inspired algorithms in the 32 QAM modulation scheme.

关键词： Simultaneous wireless information and power transfer (SWIPT) power-splitting IQ imbalance hardware impairments bio-inspired algorithms particle swarm optimization (PSO) artificial bee colony (ABC) firefly algorithm (FA) invasive weed optimization (IWO) energy harvesting

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Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms

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APPLIED SOFT COMPUTING 2014年 23卷 474-482页

作者： Trejo Zuniga, Elmer Cesar Lopez Cruz, Irineo Lorenzo Ruiz Garcia, Agustin Univ Chapingo Dept Agr Engn Chapingo 56230 Estado De Mexic Mexico

All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system. The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it. However, because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated. This paper presents a comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) on parameter estimation of crop growth SUCROS (a Simple and Universal CROp Growth Simulator) model. Subsequently, the SUCROS model for potential growth was applied to a husk tomato crop (Physalis ixocarpa Brot. ex Horm.) using data coming from an experiment carried out in Chapingo, Mexico. The objective was to determine which algorithm generates parameter values that give the best prediction of the model. An analysis of variance (ANOVA) was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms. Algorithm's efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum. On the other hand, the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum. Simulation results showed that standard DE/rand/1/bin got the best result. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Parameter estimation Evolutionary algorithms bio-inspired algorithms SUCROS model

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Unsupervised modelling of rice aroma change during ageing based on electronic nose coupled with bio-inspired algorithms

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bioSYSTEMS ENGINEERING 2022年第0期216卷 132-146页

作者： Rahimzadeh, Hassan Sadeghi, Morteza Mireei, Seyed Ahmad Ghasemi-Varnamkhasti, Mahdi Isfahan Univ Technol Coll Agr Dept Biosyst Engn Esfahan *** Iran Shahrekord Univ Fac Agr Dept Mech Engn Biosyst Shahrekord Iran

Rice aroma profiles, consisting of different sorts of volatile organic compounds, go through complex alterations during storage period that affect its end-use quality. The aroma active compounds of rice are the key elements to determine its quality characteristics and freshness. This study evaluates the efficiency of electronic nose combined with modern biologically inspired unsupervised algorithms as a fast, real-time, and non-invasive technology for modelling rice aroma change during storage. The developed unsupervised algorithms could present successful results in clustering of the aroma change, and also could provide supplementary facilities for assessment of ageing in aromatic and non-aromatic rice samples. Self-organizing map neural network resulted in reliable grouping of the samples. The provided unified distance matrices were applied to interpret the pace of the aroma change. Fuzzy c-means was hybridized with differential evolution, and the fuzzifier parameter was applied for controlling of the samples' memberships to the storage clusters. The developed fuzzy clustering proposed more reasonable solutions when there were overlappings among the clusters. The automatic clustering developed by artificial bee colony algorithm proposed original grouping of the aromatic samples. For the nonaromatic rice, the structures obtained by the automatic approach agreed satisfactorily with the aroma change during ageing. The automatic clustering eliminated the need for the number of clusters as well as the descriptive labelled samples, and presented smarter performance. The electronic nose system combined with the developed unsupervised algorithms could be utilized as a reliable and rapid tool for analysis of rice aroma change. (C) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Automatic clustering bio-inspired algorithms Gas sensors Machine learning Postharvest technology Unsupervised modelling

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Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot

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INFORMATION SCIENCES 2012年 192卷 19-38页

作者： Castillo, Oscar Martinez-Marroquin, Ricardo Melin, Patricia Valdez, Fevrier Soria, Jose Tijuana Inst Technol Tijuana Mexico

In this paper we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) on the optimization of the membership functions' parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an autonomous wheeled mobile robot. The results obtained by the simulations performed are statistically compared among them and the previous work results obtained with GAs in order to find which is the best optimization technique for this particular robotics problem. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Fuzzy control Particle Swarm Optimization Ant Colony Optimization Autonomous robots bio-inspired algorithms

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Optimal Placement of Drone Delivery Stations and Demand Allocation using bio-inspired algorithms

Optimal Placement of Drone Delivery Stations and Demand Allo...

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IEEE International Conference on Smart Mobility (SM)

作者： Elsaid, Feras Sanchez, Enrique Torres Li, Yilun Khamis, Alaa Univ Toronto Civil & Mineral Engn Toronto ON Canada Univ Toronto Elect & Comp Engn Toronto ON Canada Gen Motors Canada Canadian Tech Ctr Oshawa ON Canada

ISBN: (纸本)9798350312751

The last-mile delivery problem has recently received more attention due to dramatic increase in e-commerce and one-day delivery options. One delivery method has been in the forefront of last mile delivery research: drone delivery. Particularly, hybrid truck-drone delivery systems which attempt to overcome the limitations of only using drones. Researchers have attempted to improve drone routing and scheduling, but not many have studied the required infrastructure, including drone stations for recharging and pick-up of packages. This paper tackles the placement of drone delivery stations using bio-inspired optimization algorithms. The solution framework consists of two stages. The first stage tackles the location planning problem of stations, while the second stage deals with the allocation of delivery demand to located stations. The conventional k-means algorithm is used as a baseline for the location planning problem, while the greedy algorithm is used as a baseline for the demand allocation problem. The results shows that the simulated annealing algorithm achieves a 14% reduction in total cost and 6.2x run time improvement, whereas the genetic algorithm achieves a reduction of 10% in total cost and 1.8x run time improvement.

关键词： Last-mile delivery optimal placement delivery drones optimization bio-inspired algorithms

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Evaluation of bio-inspired algorithms in Cluster-Based Kriging Optimization 19th

Evaluation of Bio-Inspired Algorithms in Cluster-Based Krigi...

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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Yasojima, Carlos Ramos, Tamara Araujo, Tiago Meiguins, Bianchi Neto, Nelson Morais, Jefferson Fed Univ Para Postgrad Program Comp Sci Belem Para Brazil

ISBN: (纸本)9783030242893

Kriging is one of the most used spatial estimation methods in real-world applications. In kriging estimation, some parameters must be estimated in order to reach a good accuracy in the interpolation process, however, this step is still a challenge. Various optimization methods have been tested to find good parameters to this process, however, in recent years, many authors are using bioinspired techniques and reaching good results in estimating these parameters. This paper presents a comparison between well-known bio-inspired techniques such as Genetic algorithms, Differential Evolution and Particle Swarm Optimization in the estimation of the essential kriging parameters: nugget, sill, range, angle, and factor. We also proposed an improved cluster-based kriging method to perform the tests. The results shows that the algorithms have a similar accuracy in estimating these parameters, and the number of clusters have a high impact on the results.

关键词： bio-inspired algorithms Artificial Intelligence Geostatistic Kriging

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Application of bio-inspired algorithms to the Cryptanalysis of Asymmetric Ciphers on the Basis of Composite Number

Application of Bio-inspired Algorithms to the Cryptanalysis ...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Shoshina, Anastasiia, V Borzunov, Georgii, I Ivanova, Ekaterina Y. Natl Res Nucl Univ MEPhI Moscow Russia

ISBN: (纸本)9781665404761

In some cases, the confidentiality of cryptographic algorithms used in digital communication is related to computational complexity mathematical problems, such as calculating the discrete logarithm, the knapsack problem, decomposing a composite number into prime divisors etc. This article describes the application of insolvability of factorization of a large composite number, and reviews previous work integer factorization using either the deterministic or the bio-inspired algorithms. This article focuses on the possibility of using bio-inspired methods to solve the problem of cryptanalysis of asymmetric encryption algorithms, which ones based on factorization of composite numbers. The purpose of this one is to reviewing previous work in integer factorization algorithms, developing a prototype of either the deterministic and the bio-inspired algorithm and the effectiveness of the developed algorithms and recommendations are made for future research paths.

关键词： bio-inspired algorithms integer factorization fitness function genetic algorithm RSA quadratic sieve

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